Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Initial approaches to the application of islands-based parallel EDAs in continuous domains
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Order or not: does parallelization of model building in hBOA affect its scalability?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
Parallel BMDA with an aggregation of probability models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
EDA-Based Scheduling of Users in the MIMO Multiple Access Channel
Wireless Personal Communications: An International Journal
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Estimation of Distribution Algorithms (EDAs) use a probabilistic model of promising solutions found so far to obtain new candidate solutions of an optimization problem. This paper focuses on the design of parallel EDAs. More specifically, the paper describes a method for parallel construction of Bayesian networks with local structures in form of decision trees in the Mixed Bayesian Optimization Algorithm. The proposed Multithreaded Mixed Bayesian Optimization Algorithm (MMBOA) is intended for implementation on a cluster of workstations that communicate by Message Passing Interface (MPI). Communication latencies between workstations are eliminated by multithreaded processing, so in each workstation the high-priority model-building thread, which is communication demanding, can be overlapped by low-priority model sampling thread when necessary. High performance of MMBOA is verified via simulation in TRANSIM tool.